Allowing the victim to draw a line in history: Intergroup apology effectiveness as a function of collective autonomy support
Bibliographic record
Abstract
Abstract We tested whether intergroup apology effectiveness increases when the apology is collective autonomy supportive (i.e., victimized group members are told they have the choice to accept or reject the apology). In Experiment 1, university students who received a collective autonomy supportive (compared to a collective autonomy unsupportive or basic) apology for derogatory remarks made by a rival university perceived the apology as more empathic. This, in turn, heightened intergroup forgiveness. Experiment 2 replicated and extended this effect in the context of the friendly fire killing of Canadian soldiers in Afghanistan by the United States. Canadians in the collective autonomy supportive condition felt more empowered and were less critical of the apology. Sequential mediation analyses revealed that collective autonomy support had an indirect effect on intergroup forgiveness through empowerment and empathic support of the apology. Findings suggest the apology–forgiveness link strengthens when the victimized group's collective autonomy is explicitly acknowledged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".